import numpy as np
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt

def plot_decision_regions(X, y, classifier, resolution=0.02):
    markers = ('s', 'x', 'o', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])
    x1_min, x1_max = X[:,0].min() - 1, X[:, 0].max()
    x2_min, x2_max = X[:,1].min() - 1, X[:, 1].max()

    print(x1_min, x1_max)
    print(x2_min, x2_max)

    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))

    # print(np.arange(x1_min, x1_max, resolution).shape)
    # print(np.arange(x1_min, x1_max, resolution))
    # print(xx1.shape)
    # print(xx1)
    #
    # print(np.arange(x2_min, x2_max, resolution).shape)
    # print(np.arange(x2_min, x2_max, resolution))
    # print(xx2.shape)
    # print(xx2)

    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    print(xx1.ravel())
    print(xx2.ravel())
    print(Z)

    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())

    for idx, cl in enumerate(np.unique(y)):
        ycl = (y == cl)
        ycl = True
        x1 = X[ycl, 0]
        y1 = X[ycl, 1]
        plt.scatter(x = x1, y = y1, alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl)